
The Adaptive Metaheuristic Framework (AMF) can intelligently adapt to changes in the optimization landscape, enabling it to maintain high-quality solutions despite frequent and unpredictable changes in problem parameters, constraints, and objectives.


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an-adaptive-metaheuristic-framework-for-optimizing-dynamic-problems


An Adaptive Metaheuristic Framework for Optimizing Dynamic Problems


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These lecture notes provide a rigorous yet accessible introduction to dynamic optimization, emphasizing the theoretical foundations of dynamic programming, particularly for upper semi-continuous models, and their applications in various fields, including an introduction to reinforcement learning.


lecture-notes-a-comprehensive-introduction-to-dynamic-optimization-with-a-focus-on-upper-semi-continuous-models-and-reinforcement-learning


Lecture Notes: A Comprehensive Introduction to Dynamic Optimization with a Focus on Upper Semi-Continuous Models and Reinforcement Learning
